A/B testing and the G-test
Marton Trencseni - Mon 23 March 2020 • Tagged with ab-testing
The G-test for conversion A/B tests is similar to the Chi-squared test. Monte-Carlo simulations show that the two are indistinguishable in practice.
A/B testing and networks effects
Marton Trencseni - Sat 21 March 2020 • Tagged with ab-testing
I use Monte Carlo simulations to explore how A/B testing on Watts–Strogatz random graphs depends on the degree distribution of the social network.
A/B testing on social networks
Marton Trencseni - Mon 09 March 2020 • Tagged with ab-testing
I use Monte Carlo simulations to show that experimentation on social networks is a beatiful statistical problem with unexpected nuances due to network effects.
Early stopping in A/B testing
Marton Trencseni - Thu 05 March 2020 • Tagged with ab-testing
Increased false positive rate due to early stopping is beautiful nuance of statistical testing. It is equivalent to running at an overall higher alpha. Data scientists need to be aware of this phenomenon so they can control it and keep their organizations honest about their experimental results.
A/B testing and Fisher's exact test
Marton Trencseni - Tue 03 March 2020 • Tagged with ab-testing
Fisher’s exact test directly computes the same p value as the Chi-squared test, so it does not rely on the Central Limit Theorem to hold.
A/B testing and the Chi-squared test
Marton Trencseni - Fri 28 February 2020 • Tagged with ab-testing
In an ealier post, I wrote about A/B testing conversion data with the Z-test. The Chi-squared test is a more general test for conversion data, because it can work with multiple conversion events and multiple funnels being tested (A/B/C/D/..).
A/B testing and the t-test
Marton Trencseni - Sun 23 February 2020 • Tagged with ab-testing
The t-test is better than the z-test for timespent A/B tests, because it explicitly models the uncertainty of the variance due to sampling. Using Monte-Carlo simulations I show that around N=100, the t-test becomes the z-test.
A/B testing and the Z-test
Marton Trencseni - Sat 15 February 2020 • Tagged with ab-testing
I discuss the Z-test for A/B testing and show how to compute parameters such as sample size from first principles. I use Monte Carlo simulations to validate significance level and statistical power, and visualize parameter scaling behaviour.
Beyond the Central Limit Theorem
Marton Trencseni - Thu 06 February 2020 • Tagged with data, ab testing, statistics
In the previous post, I talked about the importance of the Central Limit Theorem (CLT) to A/B testing. Here we will explore cases when we cannot rely on the CLT to hold.
A/B testing and the Central Limit Theorem
Marton Trencseni - Wed 05 February 2020 • Tagged with data, ab testing, statistics
When working with hypothesis testing, the desciptions of the statistical method often has normality assumptions. For example, the Wikipedia page for the z-test starts like this: "A Z-test is any statistical test for which the distribution of the test statistic under the null hypothesis can be approximated by a normal distribution". What does this mean? How do I know it’s a valid assumption for my data?
Optimizing waits in Airflow
Marton Trencseni - Sat 01 February 2020 • Tagged with data, airflow, python
Sometimes I get to put on my Data Engineering hat for a few days. I enjoy this because I like to move up and down the Data Science stack and I try to keep myself sharp technically. Recently I was able to spend a few days optimizing our Airflow ETL for speed.
SQL best practices for Data Scientists and Analysts
Marton Trencseni - Sun 26 January 2020 • Tagged with data, programming, sql
My list of SQL best practices for Data Scientists and Analysts, or, how I personally write SQL code. I picked this up at Facebook, and later improved it at Fetchr.
How I write SQL code
Marton Trencseni - Fri 24 January 2020 • Tagged with data, programming, sql
This is a simple post about SQL code formatting. Most of this comes from my time as a Data Engineer at Facebook.
Small team planning
Marton Trencseni - Fri 10 January 2020 • Tagged with planning, teams, goaling
I’ve worked at 5-10 different organizations, most of them were startups or startuppy companies. I’ve done a lot of planning in small teams, and also taken part in company-wide leadership planning. Here I will describe what has worked well for me in small team settings, focusing on time estimation.
Personal goaling
Marton Trencseni - Sun 22 December 2019 • Tagged with self help, goaling
The meta-goal of goaling is to stretch yourself to achieve more, and to feel good about what you’ve achieved. Whatever happened this year, it’s always possible to achieve a lot more and feel better about yourself next year. To hijack a Feynman quote, there is plenty of room at the top.
Pytorch in 2019
Marton Trencseni - Thu 12 December 2019 • Tagged with pytorch
2019 was another big year for Pytorch, one of the most popular Deep Learning libraries out there. Pytorch has become the de facto deep learning library used for research thanks to it’s dynamic graph model which allows fast model experimentation. It’s also become production ready, with support for mobile and infrastructure tooling such as Tensorboard.
Warren Buffett style fundamental metrics of long-term company performance
Marton Trencseni - Mon 02 December 2019 • Tagged with investing, stocks, warren buffett
I look at some fundamental charts of Apple, Activision Blizzard and Intel.
Calibration curves for delivery prediction with Scikit-Learn
Marton Trencseni - Thu 21 November 2019 • Tagged with machine, learning, fetchr, skl, calibration
I show calibration curves for four different binary classification Scikit-Learn models we built for delivery prediction at Fetchr, trained using real-world data: LogisticRegression, DecisionTree, RandomForest and GradientBoosting. 
Using simulated self-play to solve all OpenAI Gym classic control problems with Pytorch
Marton Trencseni - Thu 14 November 2019 • Tagged with python, pytorch, reinforcement, learning, openai, gym
I use simulated self-play by ranking episodes by summed reward. Game outcomes are divided in two by cutting at the median, winners are assigned +1 rewards, losers are assigned -1 rewards, like in games like Go and Chess. Unlike naive policy gradient descent used in previous posts, this version solves all OpenAI classic control problems, albeit slowly.
Applying policy gradient to OpenAI Gym classic control problems with Pytorch
Marton Trencseni - Tue 12 November 2019 • Tagged with python, pytorch, reinforcement, learning, openai, gym
I try to generalize the policy gradient algorithm as introduced earlier to solve all the OpenAI classic control problems. It works for CartPole and Acrobot, but not for Pendulum and MountainCar environments.
